pygtc is compatible with Python 2.7 and 3.6 and requires the following packages:
- numpy >= 1.5
- matplotlib >= 1.5.3 (preferably >= 2.0)
- scipy (optional)
- nose (optional – only needed for running unit tests)
- nose-exclude (optional – only needed for running unit tests)
Downloading and installing¶
The latest stable version of pygtc is hosted at PyPi.
If you like pip, you can install with:
pip install pygtc
Or, alternatively, download and extract the tarball from the above link and then install from source with:
cd /path/to/pygtc python setup.py install
Development happens at github. You can clone the repo with:
git clone https://github.com/SebastianBocquet/pygtc
And you can install it in developer mode with pip:
pip install -e /path/to/pygtc
or from source:
cd /path/to/pygtc python setup.py develop
For tests to for sure run properly, you’ll want to have matplotlib v2 or
greater installed. If you are using an earlier version you’ll need at least
matplotlib v1.5.3, as they fixed a bug in their
but several tests will fail anyhow due to slightly different image sizes (v1.5
adds 5 pixels in the x-dimension for some reason). You’ll need
installed to run the tests, although pygtc functions fine without it. You should
nose-exclude to make sure that the right matplotlib backend
gets loaded with the tests. You also should have the Arial font installed, as
that is pygtc’s default font and tests will “fail” if matplotlib falls back on
Bitstream Vera Sans (even though the images produced might look perfectly fine).
Test base images were produced on Mac OSX using the
Agg backend and if you
are on another system there is no guarantee that you will get a pixel-perfect
copy of what the Agg backend produces. However, the images produced by the tests
should still look great! They are saved to a folder called
whatever directory you ran the tests from.
There are two ways to run the test suite. You can use the nosetests utility from within the pygtc package directory:
cd /path/to/pygtc nosetests
Or, you can run the tests as a script:
In either case, there are 25 tests to run, and it should take between 20-30 seconds to run them all. If the first test fails, there may be something wrong with your matplotlib install in general (or maybe something weird in your rcParams). If you are missing pandas or scipy, a few tests will be skipped. If you get a ton of errors make sure you read the first paragraph in this section and you have all the prerequisites installed. If matplotlib can’t find Arial and you recently installed it, delete your matplotlib font cache and try again. If errors persist, let us know at GitHub.
Contribution and/or bug reports¶
If you like this project and want to contribute, send a message over at the gitHub repo and get involved. If you find a bug, please open an issue at gitHub. Better yet, you can try and fix the bug and submit a pull request!